Gianluca Scopelliti (Ericsson & KU Leuven), Christoph Baumann (Ericsson), Fritz Alder (KU Leuven), Eddy Truyen (KU Leuven), Jan Tobias Mühlberg (Université libre de Bruxelles & KU Leuven)

In Intelligent Transport Systems, secure communication between vehicles, infrastructure, and other road users is critical to maintain road safety. This includes the revocation of cryptographic credentials of misbehaving or malicious vehicles in a timely manner. However, current standards are vague about how revocation should be handled, and recent surveys suggest severe limitations in the scalability and effectiveness of existing revocation schemes. In this paper, we present a formally verified mechanism for self-revocation of Vehicle-to-Everything (V2X) pseudonymous credentials, which relies on a trusted processing element in vehicles but does not require a trusted time source. Our scheme is compatible with ongoing standardization efforts and, leveraging the Tamarin prover, is the first to guarantee the actual revocation of credentials with a predictable upper bound on revocation time and in the presence of realistic attackers. We test our revocation mechanism in a virtual 5G-Edge deployment scenario where a large number of vehicles communicate with each other, simulating real-world conditions such as network malfunctions and delays. Results show that our scheme upholds formal guarantees in practice, while exhibiting low network overhead and good scalability.

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SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

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GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity

Qiushi Wu (University of Minnesota), Zhongshu Gu (IBM Research), Hani Jamjoom (IBM Research), Kangjie Lu (University of Minnesota)

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CrowdGuard: Federated Backdoor Detection in Federated Learning

Phillip Rieger (Technical University of Darmstadt), Torsten Krauß (University of Würzburg), Markus Miettinen (Technical University of Darmstadt), Alexandra Dmitrienko (University of Würzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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